




"""simple docstring"""




from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


a_        =       logging.get_logger(__name__)

a_        =       {
    'shi-labs/dinat-mini-in1k-224': 'https://huggingface.co/shi-labs/dinat-mini-in1k-224/resolve/main/config.json',
    # See all Dinat models at https://huggingface.co/models?filter=dinat
}

class   UpperCAmelCase_    (			snake_case   ,					snake_case		):
		UpperCamelCase							="dinat"

		UpperCamelCase							={
		    "num_attention_heads": "num_heads",
		    "num_hidden_layers": "num_layers",
		}
		def __init__(						self				,						UpperCamelCase_=4				,						UpperCamelCase_=3				,						UpperCamelCase_=64				,						UpperCamelCase_=[3, 4, 6, 5]				,						UpperCamelCase_=[2, 4, 8, 16]				,						UpperCamelCase_=7				,						UpperCamelCase_=[[1, 8, 1], [1, 4, 1, 4], [1, 2, 1, 2, 1, 2], [1, 1, 1, 1, 1]]				,						UpperCamelCase_=3.0				,						UpperCamelCase_=True				,						UpperCamelCase_=0.0				,						UpperCamelCase_=0.0				,						UpperCamelCase_=0.1				,						UpperCamelCase_="gelu"				,						UpperCamelCase_=0.0_2				,						UpperCamelCase_=1E-5				,						UpperCamelCase_=0.0				,						UpperCamelCase_=None				,						UpperCamelCase_=None				,						**UpperCamelCase_				,						)						->       Optional[int]:
					super().__init__(**UpperCamelCase_       )

					__lowercase			:  List[Any]								=       patch_size
					__lowercase			:  List[str]								=       num_channels
					__lowercase			:  Union[str, Any]								=       embed_dim
					__lowercase			:  Tuple								=       depths
					__lowercase			:  Optional[Any]								=       len(UpperCamelCase_       )
					__lowercase			:  List[str]								=       num_heads
					__lowercase			:  Union[str, Any]								=       kernel_size
					__lowercase			:  Optional[Any]								=       dilations
					__lowercase			:  Union[str, Any]								=       mlp_ratio
					__lowercase			:  Union[str, Any]								=       qkv_bias
					__lowercase			:  Any								=       hidden_dropout_prob
					__lowercase			:  Optional[Any]								=       attention_probs_dropout_prob
					__lowercase			:  Optional[int]								=       drop_path_rate
					__lowercase			:  List[Any]								=       hidden_act
					__lowercase			:  Dict								=       layer_norm_eps
					__lowercase			:  Dict								=       initializer_range
					# we set the hidden_size attribute in order to make Dinat work with VisionEncoderDecoderModel
					# this indicates the channel dimension after the last stage of the model
					__lowercase			:  Optional[Any]								=       int(embed_dim * 2 ** (len(UpperCamelCase_       ) - 1)       )
					__lowercase			:  Any								=       layer_scale_init_value
					__lowercase			:  Tuple								=       ['''stem'''] + [F"""stage{idx}""" for idx in range(1				,						len(UpperCamelCase_       ) + 1       )]
					__lowercase			,__lowercase			:  List[str]								=       get_aligned_output_features_output_indices(
					    out_features=UpperCamelCase_				,						out_indices=UpperCamelCase_				,						stage_names=self.stage_names       )





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowercase    :   int						=			{
    'configuration_conditional_detr': [
        'CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'ConditionalDetrConfig',
        'ConditionalDetrOnnxConfig',
    ]
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase    :   List[str]						=			['ConditionalDetrFeatureExtractor']
		lowercase    :   int						=			['ConditionalDetrImageProcessor']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase    :   Optional[int]						=			[
		    'CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'ConditionalDetrForObjectDetection',
		    'ConditionalDetrForSegmentation',
		    'ConditionalDetrModel',
		    'ConditionalDetrPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_conditional_detr import (
		    CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    ConditionalDetrConfig,
		    ConditionalDetrOnnxConfig,
		)

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
				from .image_processing_conditional_detr import ConditionalDetrImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_conditional_detr import (
				    CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ConditionalDetrForObjectDetection,
				    ConditionalDetrForSegmentation,
				    ConditionalDetrModel,
				    ConditionalDetrPreTrainedModel,
				)

else:
		import sys

		lowercase    :   Optional[int]						=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)